Spatiotemporal Land Use Regression Models of Fine, Ultrafine, and Black Carbon Particulate Matter in New Delhi, India

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• 作者：Arvind Saraswat ; Joshua S. Apte ; Milind Kandlikar ; Michael Brauer ; Sarah B. Henderson ; Julian D. Marshall
• 刊名：Environmental Science & Technology
• 出版年：2013
• 出版时间：November 19, 2013
• 年：2013
• 卷：47
• 期：22
• 页码：12903-12911
• 全文大小：459K
• 年卷期：v.47,no.22(November 19, 2013)
• ISSN：1520-5851

文摘

Air pollution in New Delhi, India, is a significant environmental and health concern. To assess determinants of variability in air pollutant concentrations, we develop land use regression (LUR) models for fine particulate matter (PM_2.5), black carbon (BC), and ultrafine particle number concentrations (UFPN). We used 136 h (39 sites), 112 h (26 sites), 147 h (39 sites) of PM_2.5, BC, and UFPN data respectively, to develop separate morning (0800鈥?200) and afternoon (1200鈥?800) models. Continuous measurements of PM_2.5 and BC were also made at a single fixed rooftop site located in a high-income residential neighborhood. No continuous measurements of UFPN were available. In addition to spatial variables, measurements from the fixed continuous monitoring site were used as independent variables in the PM_2.5 and BC models. The median concentrations (and interquartile range) of PM_2.5, BC, and UFPN at LUR sites were 133 (96鈥?32) 渭g m^鈥?, 11 (6鈥?1) 渭g m^鈥?, and 40 (27鈥?2) 脳 10³ cm^鈥? respectively. In addition (a) for PM_2.5 and BC, the temporal variability was higher than the spatial variability; (b) the magnitude and spatial variability in pollutant concentrations was higher during morning than during afternoon hours. Further, model R² values were higher for morning (for PM_2.5, BC, and UFPN, respectively: 0.85, 0.86, and 0.28) than for afternoon models (0.73, 0.69, and 0.23); (c) the PM_2.5 and BC concentrations measured at LUR sites all over the city were strongly correlated with measured concentrations at a fixed rooftop site; (d) spatial patterns were similar for PM_2.5 and BC but different for UFPN; (e) population density and road variables were statistically significant predictors of pollutant concentrations; and (f) available geographic predictors explained a much lower proportion of variability in measured PM_2.5, BC, and UFPN than observed in other LUR studies, indicating the importance of temporal variability and suggesting the existence of uncharacterized sources.